ArticleBMC cancer2021
Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Meta-Analysis of CYP1A1 MspI and Ile462Val Polymorphisms in Cancer Susceptibility Among Different Ethnic Populations.Environmental and molecular mutagenesis · 2026Pooled it
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.Journal of inflammation research · 2026Article
- Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.The breast journal · 2026Article
- Mini-review: Human N-acetyltransferase genetic polymorphisms in susceptibility to aromatic amine and alkylaniline genotoxicity.The Journal of pharmacology and experimental therapeutics · 2025Review
- Triple-template surface imprinted magnetic polymers for wide-coverage extraction of steroid hormones from human serum.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025Article
- SCARB1 in extracellular vesicles promotes NPC metastasis by co-regulating M1 and M2 macrophage function.Cell death discovery · 2023Article
- The implications and prospect of cuproptosis-related genes and copper transporters in cancer progression.Frontiers in oncology · 2023Review
- Molecular Diversity and Biochemical Content in Two Invasive Alien Species: Looking for Chemical Similarities and Bioactivities.Marine drugs · 2022Article
- CYP1B1-catalyzed 4-OHE2 promotes the castration resistance of prostate cancer stem cells by estrogen receptor α-mediated IL6 activation.Cell communication and signaling : CCS · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMultiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score.
methodsUnrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve.
resultsThe estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10
conclusionEstrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.